Overview

Dataset statistics

Number of variables40
Number of observations121856
Missing cells452692
Missing cells (%)9.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory104.3 MiB
Average record size in memory897.3 B

Variable types

Numeric20
Categorical17
Boolean2
Text1

Alerts

Child_Count is highly overall correlated with Client_Family_MembersHigh correlation
Credit_Amount is highly overall correlated with Loan_AnnuityHigh correlation
Loan_Annuity is highly overall correlated with Credit_AmountHigh correlation
Age_Days is highly overall correlated with Score_Source_1High correlation
Client_Family_Members is highly overall correlated with Child_CountHigh correlation
Score_Source_1 is highly overall correlated with Age_DaysHigh correlation
Population_Region_Relative is highly overall correlated with Cleint_City_RatingHigh correlation
Cleint_City_Rating is highly overall correlated with Population_Region_RelativeHigh correlation
Accompany_Client is highly imbalanced (63.4%)Imbalance
Client_Education is highly imbalanced (52.8%)Imbalance
Loan_Contract_Type is highly imbalanced (55.1%)Imbalance
Client_Housing_Type is highly imbalanced (72.2%)Imbalance
Mobile_Tag is highly imbalanced (> 99.9%)Imbalance
Client_Permanent_Match_Tag is highly imbalanced (60.8%)Imbalance
Default is highly imbalanced (59.5%)Imbalance
Client_Income has 3622 (3.0%) missing valuesMissing
Car_Owned has 3581 (2.9%) missing valuesMissing
Bike_Owned has 3624 (3.0%) missing valuesMissing
Active_Loan has 3635 (3.0%) missing valuesMissing
House_Own has 3661 (3.0%) missing valuesMissing
Child_Count has 3638 (3.0%) missing valuesMissing
Credit_Amount has 3637 (3.0%) missing valuesMissing
Loan_Annuity has 4826 (4.0%) missing valuesMissing
Accompany_Client has 1758 (1.4%) missing valuesMissing
Client_Income_Type has 3701 (3.0%) missing valuesMissing
Client_Education has 3645 (3.0%) missing valuesMissing
Client_Marital_Status has 3473 (2.9%) missing valuesMissing
Client_Gender has 2416 (2.0%) missing valuesMissing
Loan_Contract_Type has 3651 (3.0%) missing valuesMissing
Client_Housing_Type has 3687 (3.0%) missing valuesMissing
Population_Region_Relative has 4870 (4.0%) missing valuesMissing
Age_Days has 3617 (3.0%) missing valuesMissing
Employed_Days has 24764 (20.3%) missing valuesMissing
Registration_Days has 3631 (3.0%) missing valuesMissing
ID_Days has 5985 (4.9%) missing valuesMissing
Own_House_Age has 80095 (65.7%) missing valuesMissing
Client_Occupation has 41435 (34.0%) missing valuesMissing
Client_Family_Members has 2410 (2.0%) missing valuesMissing
Cleint_City_Rating has 2409 (2.0%) missing valuesMissing
Application_Process_Day has 2428 (2.0%) missing valuesMissing
Application_Process_Hour has 3663 (3.0%) missing valuesMissing
Type_Organization has 24694 (20.3%) missing valuesMissing
Score_Source_1 has 68835 (56.5%) missing valuesMissing
Score_Source_2 has 5692 (4.7%) missing valuesMissing
Score_Source_3 has 26922 (22.1%) missing valuesMissing
Social_Circle_Default has 61928 (50.8%) missing valuesMissing
Phone_Change has 18219 (15.0%) missing valuesMissing
Credit_Bureau has 18540 (15.2%) missing valuesMissing
Client_Income is highly skewed (γ1 = 37.19911127)Skewed
ID is uniformly distributedUniform
ID has unique valuesUnique
Child_Count has 82834 (68.0%) zerosZeros
Application_Process_Day has 6287 (5.2%) zerosZeros
Credit_Bureau has 28003 (23.0%) zerosZeros

Reproduction

Analysis started2025-11-28 04:48:49.014781
Analysis finished2025-11-28 04:52:25.858150
Duration3 minutes and 36.84 seconds
Software versionydata-profiling vv4.18.0
Download configurationconfig.json

Variables

ID
Real number (ℝ)

Uniform  Unique 

Distinct121856
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12160928
Minimum12100001
Maximum12221856
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size952.1 KiB
2025-11-28T10:22:26.266731image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum12100001
5-th percentile12106094
Q112130465
median12160928
Q312191392
95-th percentile12215763
Maximum12221856
Range121855
Interquartile range (IQR)60927.5

Descriptive statistics

Standard deviation35176.942
Coefficient of variation (CV)0.0028926197
Kurtosis-1.2
Mean12160928
Median Absolute Deviation (MAD)30464
Skewness6.127353 × 10-19
Sum1.4818821 × 1012
Variance1.2374172 × 109
MonotonicityNot monotonic
2025-11-28T10:22:26.891982image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
121425091
 
< 0.1%
121859301
 
< 0.1%
122213371
 
< 0.1%
121811721
 
< 0.1%
122128261
 
< 0.1%
122104371
 
< 0.1%
122018831
 
< 0.1%
121762101
 
< 0.1%
121075121
 
< 0.1%
121335351
 
< 0.1%
Other values (121846)121846
> 99.9%
ValueCountFrequency (%)
121000011
< 0.1%
121000021
< 0.1%
121000031
< 0.1%
121000041
< 0.1%
121000051
< 0.1%
121000061
< 0.1%
121000071
< 0.1%
121000081
< 0.1%
121000091
< 0.1%
121000101
< 0.1%
ValueCountFrequency (%)
122218561
< 0.1%
122218551
< 0.1%
122218541
< 0.1%
122218531
< 0.1%
122218521
< 0.1%
122218511
< 0.1%
122218501
< 0.1%
122218491
< 0.1%
122218481
< 0.1%
122218471
< 0.1%

Client_Income
Real number (ℝ)

Missing  Skewed 

Distinct1216
Distinct (%)1.0%
Missing3622
Missing (%)3.0%
Infinite0
Infinite (%)0.0%
Mean16865.192
Minimum2565
Maximum1800009
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size952.1 KiB
2025-11-28T10:22:27.519656image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum2565
5-th percentile6750
Q111250
median14400
Q320250
95-th percentile33750
Maximum1800009
Range1797444
Interquartile range (IQR)9000

Descriptive statistics

Standard deviation11538.154
Coefficient of variation (CV)0.68414013
Kurtosis5007.6912
Mean16865.192
Median Absolute Deviation (MAD)4500
Skewness37.199111
Sum1.9940391 × 109
Variance1.3312901 × 108
MonotonicityNot monotonic
2025-11-28T10:22:28.088545image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1350013717
 
11.3%
1125011940
 
9.8%
1575010146
 
8.3%
180009514
 
7.8%
90008705
 
7.1%
225007975
 
6.5%
202506309
 
5.2%
67504190
 
3.4%
270004147
 
3.4%
81002323
 
1.9%
Other values (1206)39268
32.2%
(Missing)3622
 
3.0%
ValueCountFrequency (%)
25651
 
< 0.1%
26101
 
< 0.1%
26461
 
< 0.1%
270025
< 0.1%
27905
 
< 0.1%
28353
 
< 0.1%
2840.41
 
< 0.1%
2857.52
 
< 0.1%
2872.351
 
< 0.1%
28803
 
< 0.1%
ValueCountFrequency (%)
18000091
 
< 0.1%
6750001
 
< 0.1%
4500003
< 0.1%
395005.951
 
< 0.1%
3825001
 
< 0.1%
3375001
 
< 0.1%
2250007
< 0.1%
2160001
 
< 0.1%
2070001
 
< 0.1%
2025003
< 0.1%

Car_Owned
Categorical

Missing 

Distinct2
Distinct (%)< 0.1%
Missing3581
Missing (%)2.9%
Memory size7.0 MiB
0.0
77724 
1.0
40551 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters354825
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.077724
63.8%
1.040551
33.3%
(Missing)3581
 
2.9%

Length

2025-11-28T10:22:29.716472image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-28T10:22:30.192995image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0.077724
65.7%
1.040551
34.3%

Most occurring characters

ValueCountFrequency (%)
0195999
55.2%
.118275
33.3%
140551
 
11.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)354825
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0195999
55.2%
.118275
33.3%
140551
 
11.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)354825
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0195999
55.2%
.118275
33.3%
140551
 
11.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)354825
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0195999
55.2%
.118275
33.3%
140551
 
11.4%

Bike_Owned
Categorical

Missing 

Distinct2
Distinct (%)< 0.1%
Missing3624
Missing (%)3.0%
Memory size7.0 MiB
0.0
78948 
1.0
39284 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters354696
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.078948
64.8%
1.039284
32.2%
(Missing)3624
 
3.0%

Length

2025-11-28T10:22:30.591109image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-28T10:22:30.960344image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0.078948
66.8%
1.039284
33.2%

Most occurring characters

ValueCountFrequency (%)
0197180
55.6%
.118232
33.3%
139284
 
11.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)354696
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0197180
55.6%
.118232
33.3%
139284
 
11.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)354696
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0197180
55.6%
.118232
33.3%
139284
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)354696
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0197180
55.6%
.118232
33.3%
139284
 
11.1%

Active_Loan
Categorical

Missing 

Distinct2
Distinct (%)< 0.1%
Missing3635
Missing (%)3.0%
Memory size7.0 MiB
0.0
59208 
1.0
59013 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters354663
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.059208
48.6%
1.059013
48.4%
(Missing)3635
 
3.0%

Length

2025-11-28T10:22:31.378357image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-28T10:22:31.743876image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0.059208
50.1%
1.059013
49.9%

Most occurring characters

ValueCountFrequency (%)
0177429
50.0%
.118221
33.3%
159013
 
16.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)354663
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0177429
50.0%
.118221
33.3%
159013
 
16.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)354663
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0177429
50.0%
.118221
33.3%
159013
 
16.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)354663
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0177429
50.0%
.118221
33.3%
159013
 
16.6%

House_Own
Categorical

Missing 

Distinct2
Distinct (%)< 0.1%
Missing3661
Missing (%)3.0%
Memory size7.0 MiB
1.0
81798 
0.0
36397 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters354585
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.081798
67.1%
0.036397
29.9%
(Missing)3661
 
3.0%

Length

2025-11-28T10:22:32.217036image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-28T10:22:32.577547image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
1.081798
69.2%
0.036397
30.8%

Most occurring characters

ValueCountFrequency (%)
0154592
43.6%
.118195
33.3%
181798
23.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)354585
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0154592
43.6%
.118195
33.3%
181798
23.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)354585
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0154592
43.6%
.118195
33.3%
181798
23.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)354585
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0154592
43.6%
.118195
33.3%
181798
23.1%

Child_Count
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct14
Distinct (%)< 0.1%
Missing3638
Missing (%)3.0%
Infinite0
Infinite (%)0.0%
Mean0.41777902
Minimum0
Maximum19
Zeros82834
Zeros (%)68.0%
Negative0
Negative (%)0.0%
Memory size952.1 KiB
2025-11-28T10:22:32.895859image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum19
Range19
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.72880243
Coefficient of variation (CV)1.7444687
Kurtosis12.161459
Mean0.41777902
Median Absolute Deviation (MAD)0
Skewness2.191615
Sum49389
Variance0.53115298
MonotonicityNot monotonic
2025-11-28T10:22:33.305348image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
082834
68.0%
123431
 
19.2%
210294
 
8.4%
31430
 
1.2%
4167
 
0.1%
534
 
< 0.1%
612
 
< 0.1%
74
 
< 0.1%
144
 
< 0.1%
103
 
< 0.1%
Other values (4)5
 
< 0.1%
(Missing)3638
 
3.0%
ValueCountFrequency (%)
082834
68.0%
123431
 
19.2%
210294
 
8.4%
31430
 
1.2%
4167
 
0.1%
534
 
< 0.1%
612
 
< 0.1%
74
 
< 0.1%
82
 
< 0.1%
91
 
< 0.1%
ValueCountFrequency (%)
191
 
< 0.1%
144
 
< 0.1%
121
 
< 0.1%
103
 
< 0.1%
91
 
< 0.1%
82
 
< 0.1%
74
 
< 0.1%
612
 
< 0.1%
534
 
< 0.1%
4167
0.1%

Credit_Amount
Real number (ℝ)

High correlation  Missing 

Distinct4175
Distinct (%)3.5%
Missing3637
Missing (%)3.0%
Infinite0
Infinite (%)0.0%
Mean60046.489
Minimum4500
Maximum405000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size952.1 KiB
2025-11-28T10:22:33.800143image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum4500
5-th percentile13651.2
Q127000
median51750
Q380865
95-th percentile135000
Maximum405000
Range400500
Interquartile range (IQR)53865

Descriptive statistics

Standard deviation40350.663
Coefficient of variation (CV)0.67199039
Kurtosis1.8860045
Mean60046.489
Median Absolute Deviation (MAD)25381.35
Skewness1.2303136
Sum7.0986359 × 109
Variance1.628176 × 109
MonotonicityNot monotonic
2025-11-28T10:22:34.450154image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
450003733
 
3.1%
675003450
 
2.8%
225003127
 
2.6%
180002761
 
2.3%
270002748
 
2.3%
900002366
 
1.9%
254701759
 
1.4%
545041726
 
1.4%
808651584
 
1.3%
135001383
 
1.1%
Other values (4165)93582
76.8%
(Missing)3637
 
3.0%
ValueCountFrequency (%)
450076
0.1%
479777
0.1%
4945.510
 
< 0.1%
495013
 
< 0.1%
4975.223
 
< 0.1%
5094160
0.1%
5212.830
 
< 0.1%
5276.710
 
< 0.1%
539122
 
< 0.1%
540022
 
< 0.1%
ValueCountFrequency (%)
4050003
 
< 0.1%
403103.251
 
< 0.1%
386001.91
 
< 0.1%
329968.81
 
< 0.1%
3150006
 
< 0.1%
307557.91
 
< 0.1%
2931661
 
< 0.1%
2925001
 
< 0.1%
2700002
 
< 0.1%
26955019
< 0.1%

Loan_Annuity
Real number (ℝ)

High correlation  Missing 

Distinct10856
Distinct (%)9.3%
Missing4826
Missing (%)4.0%
Infinite0
Infinite (%)0.0%
Mean2721.2545
Minimum217.35
Maximum22500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size952.1 KiB
2025-11-28T10:22:35.101067image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum217.35
5-th percentile900
Q11657.35
median2499.75
Q33466.8
95-th percentile5345.55
Maximum22500
Range22282.65
Interquartile range (IQR)1809.45

Descriptive statistics

Standard deviation1461.4662
Coefficient of variation (CV)0.53705604
Kurtosis9.0313453
Mean2721.2545
Median Absolute Deviation (MAD)883.35
Skewness1.6801351
Sum3.1846841 × 108
Variance2135883.3
MonotonicityNot monotonic
2025-11-28T10:22:35.674348image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9002370
 
1.9%
13502091
 
1.7%
675845
 
0.7%
1012.5774
 
0.6%
3780621
 
0.5%
2621.7570
 
0.5%
1125566
 
0.5%
1237.5487
 
0.4%
3165.3475
 
0.4%
2250464
 
0.4%
Other values (10846)107767
88.4%
(Missing)4826
 
4.0%
ValueCountFrequency (%)
217.352
< 0.1%
218.72
< 0.1%
229.51
< 0.1%
241.22
< 0.1%
258.31
< 0.1%
259.651
< 0.1%
260.552
< 0.1%
274.951
< 0.1%
275.42
< 0.1%
284.41
< 0.1%
ValueCountFrequency (%)
2250014
< 0.1%
21329.11
 
< 0.1%
20821.51
 
< 0.1%
20646.452
 
< 0.1%
20616.751
 
< 0.1%
18057.62
 
< 0.1%
180001
 
< 0.1%
17782.651
 
< 0.1%
17769.61
 
< 0.1%
17370.451
 
< 0.1%

Accompany_Client
Categorical

Imbalance  Missing 

Distinct6
Distinct (%)< 0.1%
Missing1758
Missing (%)1.4%
Memory size7.2 MiB
Alone
97409 
Relative
15748 
Partner
 
4516
Kids
 
1334
Others
 
987

Length

Max length8
Median length5
Mean length5.4656947
Min length4

Characters and Unicode

Total characters656419
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAlone
2nd rowAlone
3rd rowAlone
4th rowAlone
5th rowAlone

Common Values

ValueCountFrequency (%)
Alone97409
79.9%
Relative15748
 
12.9%
Partner4516
 
3.7%
Kids1334
 
1.1%
Others987
 
0.8%
Group104
 
0.1%
(Missing)1758
 
1.4%

Length

2025-11-28T10:22:36.194157image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-28T10:22:36.669907image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
alone97409
81.1%
relative15748
 
13.1%
partner4516
 
3.8%
kids1334
 
1.1%
others987
 
0.8%
group104
 
0.1%

Most occurring characters

ValueCountFrequency (%)
e134408
20.5%
l113157
17.2%
n101925
15.5%
o97513
14.9%
A97409
14.8%
t21251
 
3.2%
a20264
 
3.1%
i17082
 
2.6%
R15748
 
2.4%
v15748
 
2.4%
Other values (10)21914
 
3.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)656419
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e134408
20.5%
l113157
17.2%
n101925
15.5%
o97513
14.9%
A97409
14.8%
t21251
 
3.2%
a20264
 
3.1%
i17082
 
2.6%
R15748
 
2.4%
v15748
 
2.4%
Other values (10)21914
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)656419
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e134408
20.5%
l113157
17.2%
n101925
15.5%
o97513
14.9%
A97409
14.8%
t21251
 
3.2%
a20264
 
3.1%
i17082
 
2.6%
R15748
 
2.4%
v15748
 
2.4%
Other values (10)21914
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)656419
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e134408
20.5%
l113157
17.2%
n101925
15.5%
o97513
14.9%
A97409
14.8%
t21251
 
3.2%
a20264
 
3.1%
i17082
 
2.6%
R15748
 
2.4%
v15748
 
2.4%
Other values (10)21914
 
3.3%

Client_Income_Type
Categorical

Missing 

Distinct8
Distinct (%)< 0.1%
Missing3701
Missing (%)3.0%
Memory size7.5 MiB
Service
61028 
Commercial
27764 
Retired
21043 
Govt Job
8303 
Student
 
8
Other values (3)
 
9

Length

Max length15
Median length7
Mean length7.7755321
Min length7

Characters and Unicode

Total characters918718
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowCommercial
2nd rowService
3rd rowService
4th rowRetired
5th rowCommercial

Common Values

ValueCountFrequency (%)
Service61028
50.1%
Commercial27764
22.8%
Retired21043
 
17.3%
Govt Job8303
 
6.8%
Student8
 
< 0.1%
Unemployed6
 
< 0.1%
Maternity leave2
 
< 0.1%
Businessman1
 
< 0.1%
(Missing)3701
 
3.0%

Length

2025-11-28T10:22:37.189732image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-28T10:22:37.679776image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
service61028
48.3%
commercial27764
22.0%
retired21043
 
16.6%
govt8303
 
6.6%
job8303
 
6.6%
student8
 
< 0.1%
unemployed6
 
< 0.1%
maternity2
 
< 0.1%
leave2
 
< 0.1%
businessman1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e191933
20.9%
i109838
12.0%
r109837
12.0%
c88792
9.7%
v69333
 
7.5%
S61036
 
6.6%
m55535
 
6.0%
o44376
 
4.8%
t29366
 
3.2%
l27772
 
3.0%
Other values (16)130900
14.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)918718
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e191933
20.9%
i109838
12.0%
r109837
12.0%
c88792
9.7%
v69333
 
7.5%
S61036
 
6.6%
m55535
 
6.0%
o44376
 
4.8%
t29366
 
3.2%
l27772
 
3.0%
Other values (16)130900
14.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)918718
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e191933
20.9%
i109838
12.0%
r109837
12.0%
c88792
9.7%
v69333
 
7.5%
S61036
 
6.6%
m55535
 
6.0%
o44376
 
4.8%
t29366
 
3.2%
l27772
 
3.0%
Other values (16)130900
14.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)918718
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e191933
20.9%
i109838
12.0%
r109837
12.0%
c88792
9.7%
v69333
 
7.5%
S61036
 
6.6%
m55535
 
6.0%
o44376
 
4.8%
t29366
 
3.2%
l27772
 
3.0%
Other values (16)130900
14.2%

Client_Education
Categorical

Imbalance  Missing 

Distinct5
Distinct (%)< 0.1%
Missing3645
Missing (%)3.0%
Memory size7.7 MiB
Secondary
83911 
Graduation
28819 
Graduation dropout
 
3960
Junior secondary
 
1455
Post Grad
 
66

Length

Max length18
Median length9
Mean length9.6314472
Min length9

Characters and Unicode

Total characters1138543
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSecondary
2nd rowGraduation
3rd rowGraduation dropout
4th rowSecondary
5th rowSecondary

Common Values

ValueCountFrequency (%)
Secondary83911
68.9%
Graduation28819
 
23.7%
Graduation dropout3960
 
3.2%
Junior secondary1455
 
1.2%
Post Grad66
 
0.1%
(Missing)3645
 
3.0%

Length

2025-11-28T10:22:38.169424image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-28T10:22:38.649724image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
secondary85366
69.0%
graduation32779
 
26.5%
dropout3960
 
3.2%
junior1455
 
1.2%
post66
 
0.1%
grad66
 
0.1%

Most occurring characters

ValueCountFrequency (%)
a150990
13.3%
o127586
11.2%
r123626
10.9%
d122171
10.7%
n119600
10.5%
c85366
7.5%
y85366
7.5%
e85366
7.5%
S83911
7.4%
u38194
 
3.4%
Other values (8)116367
10.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)1138543
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a150990
13.3%
o127586
11.2%
r123626
10.9%
d122171
10.7%
n119600
10.5%
c85366
7.5%
y85366
7.5%
e85366
7.5%
S83911
7.4%
u38194
 
3.4%
Other values (8)116367
10.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1138543
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a150990
13.3%
o127586
11.2%
r123626
10.9%
d122171
10.7%
n119600
10.5%
c85366
7.5%
y85366
7.5%
e85366
7.5%
S83911
7.4%
u38194
 
3.4%
Other values (8)116367
10.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1138543
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a150990
13.3%
o127586
11.2%
r123626
10.9%
d122171
10.7%
n119600
10.5%
c85366
7.5%
y85366
7.5%
e85366
7.5%
S83911
7.4%
u38194
 
3.4%
Other values (8)116367
10.2%

Client_Marital_Status
Categorical

Missing 

Distinct4
Distinct (%)< 0.1%
Missing3473
Missing (%)2.9%
Memory size6.7 MiB
M
87349 
S
17404 
D
 
7556
W
 
6074

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters118383
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowM
2nd rowM
3rd rowW
4th rowM
5th rowM

Common Values

ValueCountFrequency (%)
M87349
71.7%
S17404
 
14.3%
D7556
 
6.2%
W6074
 
5.0%
(Missing)3473
 
2.9%

Length

2025-11-28T10:22:39.276340image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-28T10:22:39.682853image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
m87349
73.8%
s17404
 
14.7%
d7556
 
6.4%
w6074
 
5.1%

Most occurring characters

ValueCountFrequency (%)
M87349
73.8%
S17404
 
14.7%
D7556
 
6.4%
W6074
 
5.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)118383
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
M87349
73.8%
S17404
 
14.7%
D7556
 
6.4%
W6074
 
5.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)118383
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
M87349
73.8%
S17404
 
14.7%
D7556
 
6.4%
W6074
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)118383
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
M87349
73.8%
S17404
 
14.7%
D7556
 
6.4%
W6074
 
5.1%

Client_Gender
Categorical

Missing 

Distinct2
Distinct (%)< 0.1%
Missing2416
Missing (%)2.0%
Memory size7.2 MiB
Male
78463 
Female
40977 

Length

Max length6
Median length4
Mean length4.686152
Min length4

Characters and Unicode

Total characters559714
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMale
2nd rowMale
3rd rowMale
4th rowMale
5th rowFemale

Common Values

ValueCountFrequency (%)
Male78463
64.4%
Female40977
33.6%
(Missing)2416
 
2.0%

Length

2025-11-28T10:22:40.237038image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-28T10:22:40.613171image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
male78463
65.7%
female40977
34.3%

Most occurring characters

ValueCountFrequency (%)
e160417
28.7%
a119440
21.3%
l119440
21.3%
M78463
14.0%
F40977
 
7.3%
m40977
 
7.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)559714
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e160417
28.7%
a119440
21.3%
l119440
21.3%
M78463
14.0%
F40977
 
7.3%
m40977
 
7.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)559714
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e160417
28.7%
a119440
21.3%
l119440
21.3%
M78463
14.0%
F40977
 
7.3%
m40977
 
7.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)559714
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e160417
28.7%
a119440
21.3%
l119440
21.3%
M78463
14.0%
F40977
 
7.3%
m40977
 
7.3%

Loan_Contract_Type
Categorical

Imbalance  Missing 

Distinct2
Distinct (%)< 0.1%
Missing3651
Missing (%)3.0%
Memory size6.8 MiB
CL
107118 
RL
11087 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters236410
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCL
2nd rowCL
3rd rowCL
4th rowCL
5th rowCL

Common Values

ValueCountFrequency (%)
CL107118
87.9%
RL11087
 
9.1%
(Missing)3651
 
3.0%

Length

2025-11-28T10:22:41.088236image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-28T10:22:41.500546image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
cl107118
90.6%
rl11087
 
9.4%

Most occurring characters

ValueCountFrequency (%)
L118205
50.0%
C107118
45.3%
R11087
 
4.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)236410
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
L118205
50.0%
C107118
45.3%
R11087
 
4.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)236410
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
L118205
50.0%
C107118
45.3%
R11087
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)236410
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
L118205
50.0%
C107118
45.3%
R11087
 
4.7%

Client_Housing_Type
Categorical

Imbalance  Missing 

Distinct6
Distinct (%)< 0.1%
Missing3687
Missing (%)3.0%
Memory size7.1 MiB
Home
104870 
Family
 
5783
Municipal
 
4248
Rental
 
1816
Office
 
1002

Length

Max length9
Median length4
Mean length4.33293
Min length4

Characters and Unicode

Total characters512018
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHome
2nd rowHome
3rd rowFamily
4th rowHome
5th rowHome

Common Values

ValueCountFrequency (%)
Home104870
86.1%
Family5783
 
4.7%
Municipal4248
 
3.5%
Rental1816
 
1.5%
Office1002
 
0.8%
Shared450
 
0.4%
(Missing)3687
 
3.0%

Length

2025-11-28T10:22:41.854991image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-28T10:22:42.254662image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
home104870
88.7%
family5783
 
4.9%
municipal4248
 
3.6%
rental1816
 
1.5%
office1002
 
0.8%
shared450
 
0.4%

Most occurring characters

ValueCountFrequency (%)
m110653
21.6%
e108138
21.1%
H104870
20.5%
o104870
20.5%
i15281
 
3.0%
a12297
 
2.4%
l11847
 
2.3%
n6064
 
1.2%
y5783
 
1.1%
F5783
 
1.1%
Other values (12)26432
 
5.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)512018
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
m110653
21.6%
e108138
21.1%
H104870
20.5%
o104870
20.5%
i15281
 
3.0%
a12297
 
2.4%
l11847
 
2.3%
n6064
 
1.2%
y5783
 
1.1%
F5783
 
1.1%
Other values (12)26432
 
5.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)512018
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
m110653
21.6%
e108138
21.1%
H104870
20.5%
o104870
20.5%
i15281
 
3.0%
a12297
 
2.4%
l11847
 
2.3%
n6064
 
1.2%
y5783
 
1.1%
F5783
 
1.1%
Other values (12)26432
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)512018
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
m110653
21.6%
e108138
21.1%
H104870
20.5%
o104870
20.5%
i15281
 
3.0%
a12297
 
2.4%
l11847
 
2.3%
n6064
 
1.2%
y5783
 
1.1%
F5783
 
1.1%
Other values (12)26432
 
5.2%

Population_Region_Relative
Real number (ℝ)

High correlation  Missing 

Distinct80
Distinct (%)0.1%
Missing4870
Missing (%)4.0%
Infinite0
Infinite (%)0.0%
Mean0.020893211
Minimum0.000533
Maximum0.072508
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size952.1 KiB
2025-11-28T10:22:42.717979image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0.000533
5-th percentile0.00496
Q10.010006
median0.01885
Q30.028663
95-th percentile0.04622
Maximum0.072508
Range0.071975
Interquartile range (IQR)0.018657

Descriptive statistics

Standard deviation0.013924558
Coefficient of variation (CV)0.66646326
Kurtosis3.2511039
Mean0.020893211
Median Absolute Deviation (MAD)0.009193
Skewness1.4969453
Sum2444.2132
Variance0.00019389331
MonotonicityNot monotonic
2025-11-28T10:22:43.283545image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0357926267
 
5.1%
0.046225117
 
4.2%
0.0307554639
 
3.8%
0.0251644516
 
3.7%
0.0263924330
 
3.6%
0.0313294257
 
3.5%
0.0286634227
 
3.5%
0.0191013314
 
2.7%
0.0725083303
 
2.7%
0.0207133045
 
2.5%
Other values (70)73971
60.7%
(Missing)4870
 
4.0%
ValueCountFrequency (%)
0.00053317
 
< 0.1%
0.00093812
 
< 0.1%
0.001276194
 
0.2%
0.00133394
 
0.1%
0.001417189
 
0.2%
0.002042587
0.5%
0.002134395
0.3%
0.002506365
0.3%
0.003069671
0.6%
0.003122444
0.4%
ValueCountFrequency (%)
0.0725083303
2.7%
0.046225117
4.2%
0.0357926267
5.1%
0.0325612543
2.1%
0.0313294257
3.5%
0.0307554639
3.8%
0.0286634227
3.5%
0.0263924330
3.6%
0.0251644516
3.7%
0.024612437
 
2.0%

Age_Days
Real number (ℝ)

High correlation  Missing 

Distinct17000
Distinct (%)14.4%
Missing3617
Missing (%)3.0%
Infinite0
Infinite (%)0.0%
Mean16027.423
Minimum7676
Maximum25201
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size952.1 KiB
2025-11-28T10:22:43.864000image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum7676
5-th percentile9406
Q112398
median15734
Q319661
95-th percentile23209
Maximum25201
Range17525
Interquartile range (IQR)7263

Descriptive statistics

Standard deviation4366.3565
Coefficient of variation (CV)0.27243035
Kurtosis-1.0462449
Mean16027.423
Median Absolute Deviation (MAD)3625
Skewness0.12189802
Sum1.8950665 × 109
Variance19065069
MonotonicityNot monotonic
2025-11-28T10:22:44.405250image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1093622
 
< 0.1%
2019321
 
< 0.1%
1332721
 
< 0.1%
1323121
 
< 0.1%
1233421
 
< 0.1%
1520120
 
< 0.1%
1006520
 
< 0.1%
1425820
 
< 0.1%
1698720
 
< 0.1%
1340920
 
< 0.1%
Other values (16990)118033
96.9%
(Missing)3617
 
3.0%
ValueCountFrequency (%)
76762
< 0.1%
76781
 
< 0.1%
76791
 
< 0.1%
76803
< 0.1%
76831
 
< 0.1%
76871
 
< 0.1%
76882
< 0.1%
76891
 
< 0.1%
76902
< 0.1%
76912
< 0.1%
ValueCountFrequency (%)
252011
 
< 0.1%
252001
 
< 0.1%
251972
< 0.1%
251963
< 0.1%
251951
 
< 0.1%
251921
 
< 0.1%
251911
 
< 0.1%
251861
 
< 0.1%
251841
 
< 0.1%
251821
 
< 0.1%

Employed_Days
Real number (ℝ)

Missing 

Distinct9948
Distinct (%)10.2%
Missing24764
Missing (%)20.3%
Infinite0
Infinite (%)0.0%
Mean2379.6275
Minimum0
Maximum17546
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size952.1 KiB
2025-11-28T10:22:44.975744image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile207
Q1767
median1649
Q33167.25
95-th percentile7276
Maximum17546
Range17546
Interquartile range (IQR)2400.25

Descriptive statistics

Standard deviation2327.0985
Coefficient of variation (CV)0.97792555
Kurtosis4.6973702
Mean2379.6275
Median Absolute Deviation (MAD)1065
Skewness1.9571427
Sum2.3104279 × 108
Variance5415387.5
MonotonicityNot monotonic
2025-11-28T10:22:45.564626image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
38169
 
0.1%
21266
 
0.1%
23064
 
0.1%
23161
 
0.1%
11660
 
< 0.1%
19960
 
< 0.1%
21659
 
< 0.1%
76558
 
< 0.1%
41957
 
< 0.1%
22456
 
< 0.1%
Other values (9938)96482
79.2%
(Missing)24764
 
20.3%
ValueCountFrequency (%)
02
< 0.1%
22
< 0.1%
41
 
< 0.1%
61
 
< 0.1%
82
< 0.1%
91
 
< 0.1%
101
 
< 0.1%
112
< 0.1%
124
< 0.1%
131
 
< 0.1%
ValueCountFrequency (%)
175462
< 0.1%
171701
< 0.1%
171391
< 0.1%
166782
< 0.1%
166511
< 0.1%
164951
< 0.1%
164521
< 0.1%
163751
< 0.1%
163641
< 0.1%
163432
< 0.1%

Registration_Days
Real number (ℝ)

Missing 

Distinct14142
Distinct (%)12.0%
Missing3631
Missing (%)3.0%
Infinite0
Infinite (%)0.0%
Mean4975.1621
Minimum0
Maximum23738
Zeros35
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size952.1 KiB
2025-11-28T10:22:46.105546image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile332
Q12008
median4493
Q37464
95-th percentile11383.8
Maximum23738
Range23738
Interquartile range (IQR)5456

Descriptive statistics

Standard deviation3514.547
Coefficient of variation (CV)0.70641858
Kurtosis-0.31885061
Mean4975.1621
Median Absolute Deviation (MAD)2698
Skewness0.59111333
Sum5.8818854 × 108
Variance12352040
MonotonicityNot monotonic
2025-11-28T10:22:46.390374image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
145
 
< 0.1%
638
 
< 0.1%
438
 
< 0.1%
938
 
< 0.1%
236
 
< 0.1%
78435
 
< 0.1%
035
 
< 0.1%
51135
 
< 0.1%
97333
 
< 0.1%
1432
 
< 0.1%
Other values (14132)117860
96.7%
(Missing)3631
 
3.0%
ValueCountFrequency (%)
035
< 0.1%
145
< 0.1%
236
< 0.1%
327
< 0.1%
438
< 0.1%
530
< 0.1%
638
< 0.1%
732
< 0.1%
831
< 0.1%
938
< 0.1%
ValueCountFrequency (%)
237382
< 0.1%
227011
< 0.1%
218651
< 0.1%
212491
< 0.1%
208401
< 0.1%
205711
< 0.1%
205692
< 0.1%
205601
< 0.1%
205161
< 0.1%
201362
< 0.1%

ID_Days
Real number (ℝ)

Missing 

Distinct5962
Distinct (%)5.1%
Missing5985
Missing (%)4.9%
Infinite0
Infinite (%)0.0%
Mean2987.471
Minimum0
Maximum7197
Zeros6
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size952.1 KiB
2025-11-28T10:22:46.614796image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile368
Q11705
median3242
Q34295
95-th percentile4941.5
Maximum7197
Range7197
Interquartile range (IQR)2590

Descriptive statistics

Standard deviation1511.8846
Coefficient of variation (CV)0.50607506
Kurtosis-1.1132862
Mean2987.471
Median Absolute Deviation (MAD)1193
Skewness-0.34419833
Sum3.4616125 × 108
Variance2285795
MonotonicityNot monotonic
2025-11-28T10:22:47.313470image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
405376
 
0.1%
403274
 
0.1%
437573
 
0.1%
431270
 
0.1%
414469
 
0.1%
425067
 
0.1%
461966
 
0.1%
419364
 
0.1%
440464
 
0.1%
414563
 
0.1%
Other values (5952)115185
94.5%
(Missing)5985
 
4.9%
ValueCountFrequency (%)
06
 
< 0.1%
122
< 0.1%
212
 
< 0.1%
323
< 0.1%
418
< 0.1%
520
< 0.1%
625
< 0.1%
743
< 0.1%
837
< 0.1%
927
< 0.1%
ValueCountFrequency (%)
71971
< 0.1%
62742
< 0.1%
62632
< 0.1%
62351
< 0.1%
62331
< 0.1%
62281
< 0.1%
62261
< 0.1%
62232
< 0.1%
62081
< 0.1%
62031
< 0.1%

Own_House_Age
Real number (ℝ)

Missing 

Distinct55
Distinct (%)0.1%
Missing80095
Missing (%)65.7%
Infinite0
Infinite (%)0.0%
Mean12.157324
Minimum0
Maximum69
Zeros859
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size952.1 KiB
2025-11-28T10:22:48.063354image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q15
median9
Q315
95-th percentile30
Maximum69
Range69
Interquartile range (IQR)10

Descriptive statistics

Standard deviation12.056079
Coefficient of variation (CV)0.99167215
Kurtosis8.9863734
Mean12.157324
Median Absolute Deviation (MAD)5
Skewness2.724026
Sum507702
Variance145.34905
MonotonicityNot monotonic
2025-11-28T10:22:48.750567image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
73015
 
2.5%
32555
 
2.1%
62525
 
2.1%
22321
 
1.9%
82302
 
1.9%
42175
 
1.8%
92053
 
1.7%
12041
 
1.7%
101945
 
1.6%
141855
 
1.5%
Other values (45)18974
 
15.6%
(Missing)80095
65.7%
ValueCountFrequency (%)
0859
 
0.7%
12041
1.7%
22321
1.9%
32555
2.1%
42175
1.8%
51433
1.2%
62525
2.1%
73015
2.5%
82302
1.9%
92053
1.7%
ValueCountFrequency (%)
691
 
< 0.1%
65392
0.3%
64974
0.8%
632
 
< 0.1%
572
 
< 0.1%
545
 
< 0.1%
502
 
< 0.1%
491
 
< 0.1%
462
 
< 0.1%
451
 
< 0.1%

Mobile_Tag
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.7 MiB
1
121855 
0
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters121856
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1121855
> 99.9%
01
 
< 0.1%

Length

2025-11-28T10:22:49.375966image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-28T10:22:49.753271image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
1121855
> 99.9%
01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
1121855
> 99.9%
01
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)121856
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1121855
> 99.9%
01
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)121856
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1121855
> 99.9%
01
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)121856
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1121855
> 99.9%
01
 
< 0.1%

Homephone_Tag
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.7 MiB
0
97424 
1
24432 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters121856
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
097424
80.0%
124432
 
20.0%

Length

2025-11-28T10:22:50.214299image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-28T10:22:50.644545image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
097424
80.0%
124432
 
20.0%

Most occurring characters

ValueCountFrequency (%)
097424
80.0%
124432
 
20.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)121856
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
097424
80.0%
124432
 
20.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)121856
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
097424
80.0%
124432
 
20.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)121856
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
097424
80.0%
124432
 
20.0%

Workphone_Working
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.7 MiB
0
87590 
1
34266 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters121856
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
087590
71.9%
134266
 
28.1%

Length

2025-11-28T10:22:51.037735image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-28T10:22:51.435949image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
087590
71.9%
134266
 
28.1%

Most occurring characters

ValueCountFrequency (%)
087590
71.9%
134266
 
28.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)121856
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
087590
71.9%
134266
 
28.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)121856
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
087590
71.9%
134266
 
28.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)121856
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
087590
71.9%
134266
 
28.1%

Client_Occupation
Categorical

Missing 

Distinct18
Distinct (%)< 0.1%
Missing41435
Missing (%)34.0%
Memory size7.2 MiB
Laborers
21024 
Sales
12136 
Core
10611 
Managers
8099 
Drivers
7150 
Other values (13)
21401 

Length

Max length18
Median length15
Mean length7.649967
Min length2

Characters and Unicode

Total characters615218
Distinct characters35
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSales
2nd rowRealty agents
3rd rowLaborers
4th rowLaborers
5th rowSales

Common Values

ValueCountFrequency (%)
Laborers21024
17.3%
Sales12136
 
10.0%
Core10611
 
8.7%
Managers8099
 
6.6%
Drivers7150
 
5.9%
High skill tech4317
 
3.5%
Accountants3766
 
3.1%
Medicine3172
 
2.6%
Security2683
 
2.2%
Cooking2224
 
1.8%
Other values (8)5239
 
4.3%
(Missing)41435
34.0%

Length

2025-11-28T10:22:51.927056image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
laborers21811
23.9%
sales12136
13.3%
core10611
11.6%
managers8099
 
8.9%
drivers7150
 
7.8%
high4317
 
4.7%
skill4317
 
4.7%
tech4317
 
4.7%
accountants3766
 
4.1%
medicine3172
 
3.5%
Other values (12)11432
12.5%

Most occurring characters

ValueCountFrequency (%)
r83411
13.6%
e81066
13.2%
s60394
 
9.8%
a58752
 
9.5%
o41423
 
6.7%
i32598
 
5.3%
n25262
 
4.1%
l24346
 
4.0%
L22598
 
3.7%
b22322
 
3.6%
Other values (25)163046
26.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)615218
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r83411
13.6%
e81066
13.2%
s60394
 
9.8%
a58752
 
9.5%
o41423
 
6.7%
i32598
 
5.3%
n25262
 
4.1%
l24346
 
4.0%
L22598
 
3.7%
b22322
 
3.6%
Other values (25)163046
26.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)615218
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r83411
13.6%
e81066
13.2%
s60394
 
9.8%
a58752
 
9.5%
o41423
 
6.7%
i32598
 
5.3%
n25262
 
4.1%
l24346
 
4.0%
L22598
 
3.7%
b22322
 
3.6%
Other values (25)163046
26.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)615218
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r83411
13.6%
e81066
13.2%
s60394
 
9.8%
a58752
 
9.5%
o41423
 
6.7%
i32598
 
5.3%
n25262
 
4.1%
l24346
 
4.0%
L22598
 
3.7%
b22322
 
3.6%
Other values (25)163046
26.5%

Client_Family_Members
Real number (ℝ)

High correlation  Missing 

Distinct15
Distinct (%)< 0.1%
Missing2410
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean2.1543292
Minimum1
Maximum16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size952.1 KiB
2025-11-28T10:22:52.339345image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q33
95-th percentile4
Maximum16
Range15
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.91268564
Coefficient of variation (CV)0.4236519
Kurtosis3.0826497
Mean2.1543292
Median Absolute Deviation (MAD)0
Skewness1.0338185
Sum257326
Variance0.83299507
MonotonicityNot monotonic
2025-11-28T10:22:52.769678image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
261652
50.6%
126213
21.5%
320434
 
16.8%
49583
 
7.9%
51349
 
1.1%
6157
 
0.1%
732
 
< 0.1%
811
 
< 0.1%
94
 
< 0.1%
103
 
< 0.1%
Other values (5)8
 
< 0.1%
(Missing)2410
 
2.0%
ValueCountFrequency (%)
126213
21.5%
261652
50.6%
320434
 
16.8%
49583
 
7.9%
51349
 
1.1%
6157
 
0.1%
732
 
< 0.1%
811
 
< 0.1%
94
 
< 0.1%
103
 
< 0.1%
ValueCountFrequency (%)
162
 
< 0.1%
151
 
< 0.1%
141
 
< 0.1%
131
 
< 0.1%
123
 
< 0.1%
103
 
< 0.1%
94
 
< 0.1%
811
 
< 0.1%
732
 
< 0.1%
6157
0.1%

Cleint_City_Rating
Categorical

High correlation  Missing 

Distinct3
Distinct (%)< 0.1%
Missing2409
Missing (%)2.0%
Memory size7.0 MiB
2.0
88949 
3.0
17043 
1.0
13455 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters358341
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row3.0
5th row1.0

Common Values

ValueCountFrequency (%)
2.088949
73.0%
3.017043
 
14.0%
1.013455
 
11.0%
(Missing)2409
 
2.0%

Length

2025-11-28T10:22:53.260942image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-28T10:22:53.674287image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
2.088949
74.5%
3.017043
 
14.3%
1.013455
 
11.3%

Most occurring characters

ValueCountFrequency (%)
.119447
33.3%
0119447
33.3%
288949
24.8%
317043
 
4.8%
113455
 
3.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)358341
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
.119447
33.3%
0119447
33.3%
288949
24.8%
317043
 
4.8%
113455
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)358341
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
.119447
33.3%
0119447
33.3%
288949
24.8%
317043
 
4.8%
113455
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)358341
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
.119447
33.3%
0119447
33.3%
288949
24.8%
317043
 
4.8%
113455
 
3.8%

Application_Process_Day
Real number (ℝ)

Missing  Zeros 

Distinct7
Distinct (%)< 0.1%
Missing2428
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean3.1597364
Minimum0
Maximum6
Zeros6287
Zeros (%)5.2%
Negative0
Negative (%)0.0%
Memory size952.1 KiB
2025-11-28T10:22:54.055861image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q35
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.759045
Coefficient of variation (CV)0.55670624
Kurtosis-1.0914392
Mean3.1597364
Median Absolute Deviation (MAD)1
Skewness0.007727949
Sum377361
Variance3.0942392
MonotonicityNot monotonic
2025-11-28T10:22:54.431966image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
220907
17.2%
320116
16.5%
119712
16.2%
419668
16.1%
519613
16.1%
613125
10.8%
06287
 
5.2%
(Missing)2428
 
2.0%
ValueCountFrequency (%)
06287
 
5.2%
119712
16.2%
220907
17.2%
320116
16.5%
419668
16.1%
519613
16.1%
613125
10.8%
ValueCountFrequency (%)
613125
10.8%
519613
16.1%
419668
16.1%
320116
16.5%
220907
17.2%
119712
16.2%
06287
 
5.2%

Application_Process_Hour
Real number (ℝ)

Missing 

Distinct24
Distinct (%)< 0.1%
Missing3663
Missing (%)3.0%
Infinite0
Infinite (%)0.0%
Mean12.0631
Minimum0
Maximum23
Zeros26
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size952.1 KiB
2025-11-28T10:22:54.837847image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7
Q110
median12
Q314
95-th percentile17
Maximum23
Range23
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.2806946
Coefficient of variation (CV)0.27196115
Kurtosis-0.18198285
Mean12.0631
Median Absolute Deviation (MAD)2
Skewness-0.034235866
Sum1425774
Variance10.762957
MonotonicityNot monotonic
2025-11-28T10:22:55.314792image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
1014465
11.9%
1114413
11.8%
1212977
10.6%
1311765
9.7%
1410702
8.8%
910525
8.6%
159614
7.9%
167739
6.4%
175843
4.8%
85821
4.8%
Other values (14)14329
11.8%
(Missing)3663
 
3.0%
ValueCountFrequency (%)
026
 
< 0.1%
128
 
< 0.1%
2112
 
0.1%
3506
 
0.4%
4854
 
0.7%
51437
 
1.2%
62247
 
1.8%
73441
 
2.8%
85821
4.8%
910525
8.6%
ValueCountFrequency (%)
2314
 
< 0.1%
2267
 
0.1%
21164
 
0.1%
20494
 
0.4%
191464
 
1.2%
183475
 
2.9%
175843
4.8%
167739
6.4%
159614
7.9%
1410702
8.8%

Client_Permanent_Match_Tag
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size119.1 KiB
True
112454 
False
 
9402
ValueCountFrequency (%)
True112454
92.3%
False9402
 
7.7%
2025-11-28T10:22:55.697385image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size119.1 KiB
True
100015 
False
21841 
ValueCountFrequency (%)
True100015
82.1%
False21841
 
17.9%
2025-11-28T10:22:56.077868image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Type_Organization
Text

Missing 

Distinct57
Distinct (%)0.1%
Missing24694
Missing (%)20.3%
Memory size7.4 MiB
2025-11-28T10:22:56.760632image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Length

Max length22
Median length19
Mean length14.63108
Min length4

Characters and Unicode

Total characters1421585
Distinct characters50
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSelf-employed
2nd rowGovernment
3rd rowSelf-employed
4th rowBusiness Entity Type 3
5th rowOther
ValueCountFrequency (%)
type47072
20.9%
business32718
14.5%
entity32718
14.5%
329353
13.1%
self-employed14725
 
6.5%
other6290
 
2.8%
25826
 
2.6%
trade5458
 
2.4%
industry5431
 
2.4%
medicine4320
 
1.9%
Other values (39)40988
18.2%
2025-11-28T10:22:57.953408image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e167728
 
11.8%
127737
 
9.0%
t116094
 
8.2%
s115506
 
8.1%
y104149
 
7.3%
n102473
 
7.2%
i92019
 
6.5%
p65262
 
4.6%
u47183
 
3.3%
r45109
 
3.2%
Other values (40)438325
30.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)1421585
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e167728
 
11.8%
127737
 
9.0%
t116094
 
8.2%
s115506
 
8.1%
y104149
 
7.3%
n102473
 
7.2%
i92019
 
6.5%
p65262
 
4.6%
u47183
 
3.3%
r45109
 
3.2%
Other values (40)438325
30.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1421585
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e167728
 
11.8%
127737
 
9.0%
t116094
 
8.2%
s115506
 
8.1%
y104149
 
7.3%
n102473
 
7.2%
i92019
 
6.5%
p65262
 
4.6%
u47183
 
3.3%
r45109
 
3.2%
Other values (40)438325
30.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1421585
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e167728
 
11.8%
127737
 
9.0%
t116094
 
8.2%
s115506
 
8.1%
y104149
 
7.3%
n102473
 
7.2%
i92019
 
6.5%
p65262
 
4.6%
u47183
 
3.3%
r45109
 
3.2%
Other values (40)438325
30.8%

Score_Source_1
Real number (ℝ)

High correlation  Missing 

Distinct43968
Distinct (%)82.9%
Missing68835
Missing (%)56.5%
Infinite0
Infinite (%)0.0%
Mean0.50121293
Minimum0.014568132
Maximum0.94574129
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size952.1 KiB
2025-11-28T10:22:58.476318image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0.014568132
5-th percentile0.15787192
Q10.33348052
median0.50465669
Q30.67389006
95-th percentile0.83137473
Maximum0.94574129
Range0.93117316
Interquartile range (IQR)0.34040954

Descriptive statistics

Standard deviation0.21120445
Coefficient of variation (CV)0.42138668
Kurtosis-0.96855606
Mean0.50121293
Median Absolute Deviation (MAD)0.1702147
Skewness-0.067090432
Sum26574.81
Variance0.044607319
MonotonicityNot monotonic
2025-11-28T10:22:58.998960image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.5928524935
 
< 0.1%
0.6514652875
 
< 0.1%
0.4681838465
 
< 0.1%
0.5334127115
 
< 0.1%
0.7167664475
 
< 0.1%
0.2706754135
 
< 0.1%
0.4486260994
 
< 0.1%
0.8497796394
 
< 0.1%
0.3343239414
 
< 0.1%
0.5891094
 
< 0.1%
Other values (43958)52975
43.5%
(Missing)68835
56.5%
ValueCountFrequency (%)
0.0145681321
< 0.1%
0.0171765391
< 0.1%
0.0173944091
< 0.1%
0.0178968051
< 0.1%
0.0183335652
< 0.1%
0.019191381
< 0.1%
0.0194953751
< 0.1%
0.0200579081
< 0.1%
0.0221393381
< 0.1%
0.0223212911
< 0.1%
ValueCountFrequency (%)
0.9457412882
< 0.1%
0.9439822391
< 0.1%
0.9426804542
< 0.1%
0.9423331861
< 0.1%
0.9416521451
< 0.1%
0.941432721
< 0.1%
0.939511781
< 0.1%
0.9390970271
< 0.1%
0.938203181
< 0.1%
0.9360375821
< 0.1%

Score_Source_2
Real number (ℝ)

Missing 

Distinct67015
Distinct (%)57.7%
Missing5692
Missing (%)4.7%
Infinite0
Infinite (%)0.0%
Mean0.5134865
Minimum5 × 10-6
Maximum0.85499967
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size952.1 KiB
2025-11-28T10:22:59.457692image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum5 × 10-6
5-th percentile0.13208237
Q10.39015524
median0.56497276
Q30.66399408
95-th percentile0.74799392
Maximum0.85499967
Range0.85499467
Interquartile range (IQR)0.27383884

Descriptive statistics

Standard deviation0.191827
Coefficient of variation (CV)0.3735775
Kurtosis-0.30777758
Mean0.5134865
Median Absolute Deviation (MAD)0.1203024
Skewness-0.77936627
Sum59648.646
Variance0.036797599
MonotonicityNot monotonic
2025-11-28T10:23:00.014973image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.285897872269
 
0.2%
0.262258369137
 
0.1%
0.159679234135
 
0.1%
0.26525634124
 
0.1%
0.265311748116
 
0.1%
0.26314359199
 
0.1%
0.16219210687
 
0.1%
0.16214456886
 
0.1%
0.16040532185
 
0.1%
0.26651977584
 
0.1%
Other values (67005)114942
94.3%
(Missing)5692
 
4.7%
ValueCountFrequency (%)
5 × 10-61
< 0.1%
1.64 × 10-51
< 0.1%
1.67 × 10-52
< 0.1%
1.69 × 10-52
< 0.1%
2.38 × 10-51
< 0.1%
3.01 × 10-51
< 0.1%
3.49 × 10-52
< 0.1%
7.36 × 10-51
< 0.1%
7.39 × 10-51
< 0.1%
8.99 × 10-51
< 0.1%
ValueCountFrequency (%)
0.8549996668
< 0.1%
0.8213936271
 
< 0.1%
0.8206095061
 
< 0.1%
0.8204871471
 
< 0.1%
0.8185757451
 
< 0.1%
0.8184035621
 
< 0.1%
0.8173677911
 
< 0.1%
0.8156477631
 
< 0.1%
0.8156017251
 
< 0.1%
0.815374691
 
< 0.1%

Score_Source_3
Real number (ℝ)

Missing 

Distinct771
Distinct (%)0.8%
Missing26922
Missing (%)22.1%
Infinite0
Infinite (%)0.0%
Mean0.51117958
Minimum0.000527265
Maximum0.89600955
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size952.1 KiB
2025-11-28T10:23:00.620341image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0.000527265
5-th percentile0.15474363
Q10.37233367
median0.53706996
Q30.66745774
95-th percentile0.78626661
Maximum0.89600955
Range0.89548228
Interquartile range (IQR)0.29512408

Descriptive statistics

Standard deviation0.19433267
Coefficient of variation (CV)0.38016517
Kurtosis-0.64559573
Mean0.51117958
Median Absolute Deviation (MAD)0.1442961
Skewness-0.41420057
Sum48528.322
Variance0.037765189
MonotonicityNot monotonic
2025-11-28T10:23:01.351349image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.746300213567
 
0.5%
0.694092643526
 
0.4%
0.7136314509
 
0.4%
0.554946769474
 
0.4%
0.670651753470
 
0.4%
0.6577838454
 
0.4%
0.595456203438
 
0.4%
0.652896552434
 
0.4%
0.6075573432
 
0.4%
0.689479143430
 
0.4%
Other values (761)90200
74.0%
(Missing)26922
 
22.1%
ValueCountFrequency (%)
0.000527265358
0.3%
0.0170168721
 
< 0.1%
0.0186584221
 
< 0.1%
0.0201679891
 
< 0.1%
0.0214915161
 
< 0.1%
0.0219512922
 
< 0.1%
0.022420681
 
< 0.1%
0.0227390393
 
< 0.1%
0.0232248711
 
< 0.1%
0.0235543731
 
< 0.1%
ValueCountFrequency (%)
0.8960095491
 
< 0.1%
0.8939760752
 
< 0.1%
0.8854883943
 
< 0.1%
0.88253031313
< 0.1%
0.8810265754
 
< 0.1%
0.8802684818
< 0.1%
0.8795062161
 
< 0.1%
0.8787397674
 
< 0.1%
0.8771942583
 
< 0.1%
0.8748442531
 
< 0.1%

Social_Circle_Default
Real number (ℝ)

Missing 

Distinct1882
Distinct (%)3.1%
Missing61928
Missing (%)50.8%
Infinite0
Infinite (%)0.0%
Mean0.11742786
Minimum0
Maximum1
Zeros294
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size952.1 KiB
2025-11-28T10:23:01.894434image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0082
Q10.0577
median0.0887
Q30.1485
95-th percentile0.3258
Maximum1
Range1
Interquartile range (IQR)0.0908

Descriptive statistics

Standard deviation0.10797382
Coefficient of variation (CV)0.91949073
Kurtosis11.585799
Mean0.11742786
Median Absolute Deviation (MAD)0.0433
Skewness2.6564992
Sum7037.2165
Variance0.011658347
MonotonicityNot monotonic
2025-11-28T10:23:02.405333image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.08252648
 
2.2%
0.06192531
 
2.1%
0.09281763
 
1.4%
0.07221592
 
1.3%
0.00821397
 
1.1%
0.10311213
 
1.0%
0.01651207
 
1.0%
0.14851129
 
0.9%
0.01241070
 
0.9%
0.0742897
 
0.7%
Other values (1872)44481
36.5%
(Missing)61928
50.8%
ValueCountFrequency (%)
0294
0.2%
0.00165
 
0.1%
0.00153
 
< 0.1%
0.0021342
0.3%
0.00241
 
< 0.1%
0.00264
 
< 0.1%
0.0031163
0.1%
0.00341
 
< 0.1%
0.00363
 
< 0.1%
0.00382
 
< 0.1%
ValueCountFrequency (%)
154
< 0.1%
0.99073
 
< 0.1%
0.98763
 
< 0.1%
0.98144
 
< 0.1%
0.98041
 
< 0.1%
0.95621
 
< 0.1%
0.95575
 
< 0.1%
0.94854
 
< 0.1%
0.94431
 
< 0.1%
0.94021
 
< 0.1%

Phone_Change
Real number (ℝ)

Missing 

Distinct3589
Distinct (%)3.5%
Missing18219
Missing (%)15.0%
Infinite0
Infinite (%)0.0%
Mean1097.2263
Minimum1
Maximum4185
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size952.1 KiB
2025-11-28T10:23:02.935628image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile30
Q1441
median925
Q31662
95-th percentile2571
Maximum4185
Range4184
Interquartile range (IQR)1221

Descriptive statistics

Standard deviation795.9692
Coefficient of variation (CV)0.72543761
Kurtosis-0.31785401
Mean1097.2263
Median Absolute Deviation (MAD)583
Skewness0.65362721
Sum1.1371324 × 108
Variance633566.97
MonotonicityNot monotonic
2025-11-28T10:23:03.458996image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11104
 
0.9%
2916
 
0.8%
3645
 
0.5%
4524
 
0.4%
5322
 
0.3%
6216
 
0.2%
7179
 
0.1%
8118
 
0.1%
44894
 
0.1%
36491
 
0.1%
Other values (3579)99428
81.6%
(Missing)18219
 
15.0%
ValueCountFrequency (%)
11104
0.9%
2916
0.8%
3645
0.5%
4524
0.4%
5322
 
0.3%
6216
 
0.2%
7179
 
0.1%
8118
 
0.1%
972
 
0.1%
1072
 
0.1%
ValueCountFrequency (%)
41851
< 0.1%
41531
< 0.1%
41282
< 0.1%
41211
< 0.1%
40922
< 0.1%
40702
< 0.1%
40511
< 0.1%
40332
< 0.1%
40211
< 0.1%
40201
< 0.1%

Credit_Bureau
Real number (ℝ)

Missing  Zeros 

Distinct21
Distinct (%)< 0.1%
Missing18540
Missing (%)15.2%
Infinite0
Infinite (%)0.0%
Mean1.8910817
Minimum0
Maximum22
Zeros28003
Zeros (%)23.0%
Negative0
Negative (%)0.0%
Memory size952.1 KiB
2025-11-28T10:23:03.888116image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile6
Maximum22
Range22
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.8619212
Coefficient of variation (CV)0.98457999
Kurtosis2.1558673
Mean1.8910817
Median Absolute Deviation (MAD)1
Skewness1.2593187
Sum195379
Variance3.4667506
MonotonicityNot monotonic
2025-11-28T10:23:04.316573image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
028003
23.0%
124572
20.2%
219606
16.1%
313102
10.8%
47978
 
6.5%
54671
 
3.8%
62660
 
2.2%
71421
 
1.2%
8832
 
0.7%
9422
 
0.3%
Other values (11)49
 
< 0.1%
(Missing)18540
15.2%
ValueCountFrequency (%)
028003
23.0%
124572
20.2%
219606
16.1%
313102
10.8%
47978
 
6.5%
54671
 
3.8%
62660
 
2.2%
71421
 
1.2%
8832
 
0.7%
9422
 
0.3%
ValueCountFrequency (%)
221
 
< 0.1%
212
 
< 0.1%
195
< 0.1%
172
 
< 0.1%
161
 
< 0.1%
153
 
< 0.1%
146
< 0.1%
136
< 0.1%
122
 
< 0.1%
1111
< 0.1%

Default
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.7 MiB
0
112011 
1
 
9845

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters121856
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0112011
91.9%
19845
 
8.1%

Length

2025-11-28T10:23:04.808349image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-28T10:23:05.207845image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0112011
91.9%
19845
 
8.1%

Most occurring characters

ValueCountFrequency (%)
0112011
91.9%
19845
 
8.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)121856
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0112011
91.9%
19845
 
8.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)121856
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0112011
91.9%
19845
 
8.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)121856
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0112011
91.9%
19845
 
8.1%

Interactions

2025-11-28T10:22:08.879034image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:19:36.341794image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:19:43.744485image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:19:51.772813image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:19:59.928961image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:20:07.992161image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
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2025-11-28T10:20:19.503125image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:20:29.082860image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:20:37.697986image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:20:45.678009image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:20:53.521090image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:21:01.884786image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:21:09.791396image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:21:17.715933image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:21:25.450034image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:21:32.777819image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:21:41.957499image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:21:49.569603image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:21:57.314893image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
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2025-11-28T10:19:48.198113image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:19:56.215114image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:20:04.696061image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:20:12.092998image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:20:19.890990image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:20:29.542571image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:20:38.090008image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:20:46.067496image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:20:53.966719image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:21:02.291307image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:21:10.212311image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:21:18.111278image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:21:25.814220image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:21:33.139873image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:21:42.340881image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:21:49.953003image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:21:57.665102image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:22:05.590495image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:22:13.529532image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:19:40.935220image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:19:48.611584image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:19:57.143036image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:20:05.153747image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:20:12.473968image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:20:20.343614image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:20:29.939325image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:20:38.518063image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:20:46.550752image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:20:54.346838image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:21:02.682760image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:21:10.677521image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:21:18.495872image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:21:26.248117image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:21:33.504335image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:21:42.743770image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:21:50.357978image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:21:58.115657image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:22:05.985717image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:22:13.883512image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:19:41.177304image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:19:49.018259image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:19:57.520995image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:20:05.548617image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:20:12.842396image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:20:20.788040image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:20:30.323533image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:20:38.947167image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:20:46.919419image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:20:54.726816image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:21:03.060488image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:21:11.094425image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:21:18.864380image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:21:26.591364image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:21:33.849342image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:21:43.139258image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:21:50.742578image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:21:58.474267image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:22:06.427535image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:22:14.241692image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:19:41.520581image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:19:49.423434image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:19:57.908038image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:20:05.982859image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:20:13.163353image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:20:21.155110image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:20:30.681558image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:20:39.308444image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:20:47.235926image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:20:55.124610image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:21:03.411183image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:21:11.460297image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:21:19.207022image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:21:26.909688image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:21:34.214117image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:21:43.487667image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:21:51.091221image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:21:58.851447image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:22:06.756483image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:22:14.637599image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:19:41.902594image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:19:49.851587image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:19:58.298493image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:20:06.360045image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:20:13.550796image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:20:21.585980image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:20:31.154787image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:20:39.764836image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:20:47.599419image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:20:55.488160image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:21:03.745422image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:21:11.835622image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:21:19.636112image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:21:27.283902image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:21:34.550598image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:21:43.790761image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:21:51.432649image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:21:59.370731image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:22:07.166111image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:22:14.966875image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:19:42.252115image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:19:50.282435image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:19:58.696031image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:20:06.655014image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:20:13.884418image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:20:22.006222image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:20:31.542803image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:20:40.165365image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:20:47.963976image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:20:55.815957image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:21:04.150667image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:21:12.200361image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:21:19.988647image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:21:27.635272image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:21:34.999309image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:21:44.135509image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:21:51.790667image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:21:59.755706image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:22:07.470731image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:22:15.300429image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:19:42.601153image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:19:50.634196image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:19:59.045094image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:20:07.030182image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:20:14.203024image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:20:23.232950image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:20:31.896230image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:20:40.540612image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:20:48.298442image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:20:56.860766image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:21:04.505510image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:21:12.533158image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:21:20.317847image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:21:27.982809image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:21:35.320626image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:21:44.519980image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:21:52.133531image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:22:00.149152image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:22:07.837441image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:22:15.665347image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:19:43.020253image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:19:50.994791image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:19:59.391104image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:20:07.394167image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:20:14.564106image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:20:23.729867image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:20:32.295948image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:20:40.937832image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:20:48.750113image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:20:57.239722image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:21:04.854871image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:21:12.963629image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:21:20.698311image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:21:28.380117image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:21:35.683867image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:21:44.912279image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:21:52.555066image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:22:00.534654image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:22:08.164094image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:22:15.990147image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:19:43.398764image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:19:51.346583image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:19:59.684337image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:20:07.663850image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:20:14.891319image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:20:24.129773image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:20:32.660684image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:20:41.325401image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:20:49.047231image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:20:57.632275image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:21:05.178498image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:21:13.328585image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:21:21.085155image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:21:28.695374image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:21:36.922082image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:21:45.360822image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:21:52.957543image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:22:00.900293image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-11-28T10:22:08.489164image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Correlations

2025-11-28T10:23:05.617587image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
IDClient_IncomeCar_OwnedBike_OwnedActive_LoanHouse_OwnChild_CountCredit_AmountLoan_AnnuityPopulation_Region_RelativeAge_DaysEmployed_DaysRegistration_DaysID_DaysOwn_House_AgeMobile_TagHomephone_TagWorkphone_WorkingClient_Family_MembersCleint_City_RatingApplication_Process_DayApplication_Process_HourScore_Source_1Score_Source_2Score_Source_3Social_Circle_DefaultPhone_ChangeCredit_BureauDefault
ID1.0000.001-0.004-0.001-0.0010.001-0.008-0.001-0.0010.0010.0050.0020.0040.0040.004-0.002-0.0060.004-0.006-0.0020.002-0.001-0.002-0.006-0.0040.0030.0000.0020.000
Client_Income0.0011.0000.222-0.0030.0020.0080.0340.4130.4820.095-0.0840.063-0.071-0.035-0.2230.001-0.064-0.0150.038-0.229-0.0050.1020.0770.175-0.0900.1120.0460.063-0.019
Car_Owned-0.0040.2221.000-0.000-0.002-0.0080.1060.1100.1410.044-0.1290.004-0.073-0.016NaN-0.0040.011-0.0090.157-0.022-0.0050.015-0.0590.051-0.0090.0240.029-0.032-0.023
Bike_Owned-0.001-0.003-0.0001.0000.0020.0020.0030.005-0.0000.0000.000-0.000-0.0040.0000.007-0.004-0.002-0.0060.0040.000-0.0040.001-0.005-0.005-0.005-0.001-0.005-0.0010.000
Active_Loan-0.0010.002-0.0020.0021.000-0.0060.001-0.000-0.0020.0040.000-0.001-0.0000.001-0.0050.0030.0010.000-0.002-0.0030.0020.004-0.0010.0010.0030.0070.0070.0080.000
House_Own0.0010.008-0.0080.002-0.0061.000-0.007-0.038-0.0060.0230.1220.0290.017-0.002-0.009-0.002-0.112-0.0400.0070.0040.011-0.1020.0790.0020.0360.0140.0170.070-0.001
Child_Count-0.0080.0340.1060.0030.001-0.0071.000-0.0000.024-0.032-0.366-0.031-0.1750.0150.0050.0020.049-0.0350.8120.022-0.000-0.003-0.153-0.018-0.046-0.0130.013-0.0380.022
Credit_Amount-0.0010.4130.1100.005-0.000-0.038-0.0001.0000.8300.0540.0640.105-0.0050.006-0.1040.002-0.0340.0220.074-0.095-0.0040.0510.1590.1260.0230.0520.075-0.034-0.017
Loan_Annuity-0.0010.4820.141-0.000-0.002-0.0060.0240.8301.0000.055-0.0090.070-0.036-0.016-0.105-0.000-0.0310.0080.091-0.127-0.0000.0560.1120.1240.0120.0650.0760.0010.000
Population_Region_Relative0.0010.0950.0440.0000.0040.023-0.0320.0540.0551.0000.0390.0000.0350.015-0.1320.002-0.0160.065-0.021-0.428-0.0050.1280.0780.1890.0010.1570.0440.007-0.029
Age_Days0.005-0.084-0.1290.0000.0000.122-0.3660.064-0.0090.0391.0000.3050.2920.2640.0210.005-0.1670.039-0.272-0.0050.011-0.0940.5980.1020.2060.0040.1110.062-0.074
Employed_Days0.0020.0630.004-0.000-0.0010.029-0.0310.1050.0700.0000.3051.0000.1210.110-0.0460.0050.0190.0560.0170.007-0.002-0.0210.2340.1120.136-0.0000.1860.025-0.081
Registration_Days0.004-0.071-0.073-0.004-0.0000.017-0.175-0.005-0.0360.0350.2920.1211.0000.0960.039-0.000-0.0460.075-0.154-0.0690.0010.0140.1590.0650.107-0.0070.0760.019-0.037
ID_Days0.004-0.035-0.0160.0000.001-0.0020.0150.006-0.0160.0150.2640.1100.0961.0000.0090.004-0.0500.0360.0110.0120.003-0.0330.1360.0520.1390.0050.1150.032-0.056
Own_House_Age0.004-0.223NaN0.007-0.005-0.0090.005-0.104-0.105-0.1320.021-0.0460.0390.0091.000-0.006-0.016-0.0600.0070.1680.018-0.129-0.128-0.1300.023-0.0800.0440.0250.053
Mobile_Tag-0.0020.001-0.004-0.0040.003-0.0020.0020.002-0.0000.0020.0050.005-0.0000.004-0.0061.0000.0010.0020.0000.000-0.003-0.001NaNNaNNaN-0.003NaNNaN0.001
Homephone_Tag-0.006-0.0640.011-0.0020.001-0.1120.049-0.034-0.031-0.016-0.1670.019-0.046-0.050-0.0160.0011.0000.2900.0650.014-0.0160.030-0.077-0.021-0.061-0.0260.013-0.0720.022
Workphone_Working0.004-0.015-0.009-0.0060.000-0.040-0.0350.0220.0080.0650.0390.0560.0750.036-0.0600.0020.2901.000-0.013-0.078-0.0110.0550.0760.0680.0030.0560.058-0.026-0.026
Client_Family_Members-0.0060.0380.1570.004-0.0020.0070.8120.0740.091-0.021-0.2720.017-0.1540.0110.0070.0000.065-0.0131.0000.0310.002-0.013-0.098-0.000-0.025-0.0090.036-0.0190.009
Cleint_City_Rating-0.002-0.229-0.0220.000-0.0030.0040.022-0.095-0.127-0.428-0.0050.007-0.0690.0120.1680.0000.014-0.0780.0311.0000.010-0.258-0.112-0.317-0.011-0.152-0.0100.0170.059
Application_Process_Day0.002-0.005-0.005-0.0040.0020.011-0.000-0.004-0.000-0.0050.011-0.0020.0010.0030.018-0.003-0.016-0.0110.0020.0101.000-0.0250.003-0.0000.0000.002-0.0040.0060.006
Application_Process_Hour-0.0010.1020.0150.0010.004-0.102-0.0030.0510.0560.128-0.094-0.0210.014-0.033-0.129-0.0010.0300.055-0.013-0.258-0.0251.0000.0340.164-0.0380.080-0.010-0.035-0.023
Score_Source_1-0.0020.077-0.059-0.005-0.0010.079-0.1530.1590.1120.0780.5980.2340.1590.136-0.128NaN-0.0770.076-0.098-0.1120.0030.0341.0000.2260.1830.0580.1320.011-0.143
Score_Source_2-0.0060.1750.051-0.0050.0010.002-0.0180.1260.1240.1890.1020.1120.0650.052-0.130NaN-0.0210.068-0.000-0.317-0.0000.1640.2261.0000.1070.0970.164-0.025-0.143
Score_Source_3-0.004-0.090-0.009-0.0050.0030.036-0.0460.0230.0120.0010.2060.1360.1070.1390.023NaN-0.0610.003-0.025-0.0110.000-0.0380.1830.1071.0000.0040.104-0.062-0.162
Social_Circle_Default0.0030.1120.024-0.0010.0070.014-0.0130.0520.0650.1570.004-0.000-0.0070.005-0.080-0.003-0.0260.056-0.009-0.1520.0020.0800.0580.0970.0041.000-0.006-0.021-0.042
Phone_Change0.0000.0460.029-0.0050.0070.0170.0130.0750.0760.0440.1110.1860.0760.1150.044NaN0.0130.0580.036-0.010-0.004-0.0100.1320.1640.104-0.0061.0000.132-0.055
Credit_Bureau0.0020.063-0.032-0.0010.0080.070-0.038-0.0340.0010.0070.0620.0250.0190.0320.025NaN-0.072-0.026-0.0190.0170.006-0.0350.011-0.025-0.062-0.0210.1321.0000.017
Default0.000-0.019-0.0230.0000.000-0.0010.022-0.0170.000-0.029-0.074-0.081-0.037-0.0560.0530.0010.022-0.0260.0090.0590.006-0.023-0.143-0.143-0.162-0.042-0.0550.0171.000
2025-11-28T10:23:06.652431image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
IDClient_IncomeCar_OwnedBike_OwnedActive_LoanHouse_OwnChild_CountCredit_AmountLoan_AnnuityPopulation_Region_RelativeAge_DaysEmployed_DaysRegistration_DaysID_DaysOwn_House_AgeMobile_TagHomephone_TagWorkphone_WorkingClient_Family_MembersCleint_City_RatingApplication_Process_DayApplication_Process_HourScore_Source_1Score_Source_2Score_Source_3Social_Circle_DefaultPhone_ChangeCredit_BureauDefault
ID1.0000.003-0.004-0.001-0.0010.001-0.009-0.003-0.002-0.0000.0050.0030.0040.0040.004-0.002-0.0060.004-0.007-0.0020.002-0.002-0.002-0.005-0.0040.0050.0010.0030.000
Client_Income0.0031.0000.173-0.0040.0010.0040.0260.3260.4020.158-0.0520.033-0.060-0.024-0.1140.001-0.0400.0010.030-0.192-0.0020.0780.0710.132-0.0700.1030.0300.030-0.022
Car_Owned-0.0040.1731.000-0.000-0.002-0.0080.1010.1150.1410.040-0.132-0.019-0.084-0.015NaN-0.0040.011-0.0090.149-0.022-0.0050.014-0.0580.054-0.0070.0240.033-0.037-0.023
Bike_Owned-0.001-0.004-0.0001.0000.0020.0020.0020.005-0.001-0.0000.000-0.000-0.0050.0000.005-0.004-0.002-0.0060.0030.000-0.0040.002-0.005-0.005-0.005-0.005-0.006-0.0010.000
Active_Loan-0.0010.001-0.0020.0021.000-0.0060.0020.000-0.0010.0060.0010.002-0.0010.001-0.0060.0030.0010.000-0.001-0.0030.0010.004-0.0010.0020.0030.0070.0070.0090.000
House_Own0.0010.004-0.0080.002-0.0061.000-0.003-0.040-0.0030.0090.1200.0350.024-0.0070.001-0.002-0.112-0.0400.0080.0040.012-0.1050.0800.0010.0340.0100.0050.067-0.001
Child_Count-0.0090.0260.1010.0020.002-0.0031.000-0.0010.021-0.030-0.326-0.061-0.1790.0240.0040.0020.052-0.0300.8800.0250.001-0.010-0.135-0.015-0.040-0.0090.010-0.0370.020
Credit_Amount-0.0030.3260.1150.0050.000-0.040-0.0011.0000.7690.1030.0540.088-0.0090.005-0.0950.002-0.0220.0300.059-0.111-0.0050.0540.1660.1300.0410.0650.065-0.048-0.031
Loan_Annuity-0.0020.4020.141-0.001-0.001-0.0030.0210.7691.0000.119-0.0100.050-0.038-0.012-0.0950.000-0.0270.0130.072-0.1410.0000.0550.1220.1230.0260.0790.064-0.013-0.012
Population_Region_Relative-0.0000.1580.040-0.0000.0060.009-0.0300.1030.1191.0000.033-0.0060.0510.004-0.0780.002-0.0180.095-0.028-0.533-0.0060.1720.0970.201-0.0040.2080.0450.002-0.035
Age_Days0.005-0.052-0.1320.0000.0010.120-0.3260.054-0.0100.0331.0000.3490.3300.273-0.0050.005-0.1710.038-0.277-0.0050.011-0.0910.5970.0920.206-0.0020.1110.072-0.074
Employed_Days0.0030.033-0.019-0.0000.0020.035-0.0610.0880.050-0.0060.3491.0000.1670.089-0.0340.0030.0150.056-0.0290.009-0.001-0.0160.2530.0950.131-0.0010.1530.003-0.076
Registration_Days0.004-0.060-0.084-0.005-0.0010.024-0.179-0.009-0.0380.0510.3300.1671.0000.1020.0270.000-0.0590.072-0.170-0.0720.0020.0130.1840.0610.109-0.0160.0730.024-0.039
ID_Days0.004-0.024-0.0150.0000.001-0.0070.0240.005-0.0120.0040.2730.0890.1021.000-0.0050.004-0.0470.0350.0170.0110.003-0.0270.1330.0510.1380.0100.1180.033-0.054
Own_House_Age0.004-0.114NaN0.005-0.0060.0010.004-0.095-0.095-0.078-0.005-0.0340.027-0.0051.000-0.004-0.054-0.063-0.0150.0890.015-0.068-0.085-0.077-0.014-0.0500.020-0.0100.048
Mobile_Tag-0.0020.001-0.004-0.0040.003-0.0020.0020.0020.0000.0020.0050.0030.0000.004-0.0041.0000.0010.0020.0000.000-0.003-0.001NaNNaNNaN-0.000NaNNaN0.001
Homephone_Tag-0.006-0.0400.011-0.0020.001-0.1120.052-0.022-0.027-0.018-0.1710.015-0.059-0.047-0.0540.0011.0000.2900.0660.014-0.0170.037-0.076-0.021-0.062-0.0140.011-0.0770.022
Workphone_Working0.0040.001-0.009-0.0060.000-0.040-0.0300.0300.0130.0950.0380.0560.0720.035-0.0630.0020.2901.000-0.015-0.079-0.0110.0620.0770.0600.0020.0580.059-0.030-0.026
Client_Family_Members-0.0070.0300.1490.003-0.0010.0080.8800.0590.072-0.028-0.277-0.029-0.1700.017-0.0150.0000.066-0.0151.0000.0320.003-0.015-0.0960.000-0.026-0.0040.030-0.0250.011
Cleint_City_Rating-0.002-0.192-0.0220.000-0.0030.0040.025-0.111-0.141-0.533-0.0050.009-0.0720.0110.0890.0000.014-0.0790.0321.0000.010-0.267-0.113-0.289-0.008-0.175-0.0220.0130.059
Application_Process_Day0.002-0.002-0.005-0.0040.0010.0120.001-0.0050.000-0.0060.011-0.0010.0020.0030.015-0.003-0.017-0.0110.0030.0101.000-0.0260.003-0.0010.0020.001-0.0030.0030.006
Application_Process_Hour-0.0020.0780.0140.0020.004-0.105-0.0100.0540.0550.172-0.091-0.0160.013-0.027-0.068-0.0010.0370.062-0.015-0.267-0.0261.0000.0370.158-0.0400.0870.001-0.027-0.024
Score_Source_1-0.0020.071-0.058-0.005-0.0010.080-0.1350.1660.1220.0970.5970.2530.1840.133-0.085NaN-0.0760.077-0.096-0.1130.0030.0371.0000.2190.1880.0550.1380.012-0.147
Score_Source_2-0.0050.1320.054-0.0050.0020.001-0.0150.1300.1230.2010.0920.0950.0610.051-0.077NaN-0.0210.0600.000-0.289-0.0010.1580.2191.0000.1110.0890.161-0.026-0.155
Score_Source_3-0.004-0.070-0.007-0.0050.0030.034-0.0400.0410.026-0.0040.2060.1310.1090.138-0.014NaN-0.0620.002-0.026-0.0080.002-0.0400.1880.1111.0000.0050.107-0.075-0.176
Social_Circle_Default0.0050.1030.024-0.0050.0070.010-0.0090.0650.0790.208-0.002-0.001-0.0160.010-0.050-0.000-0.0140.058-0.004-0.1750.0010.0870.0550.0890.0051.0000.003-0.021-0.033
Phone_Change0.0010.0300.033-0.0060.0070.0050.0100.0650.0640.0450.1110.1530.0730.1180.020NaN0.0110.0590.030-0.022-0.0030.0010.1380.1610.1070.0031.0000.091-0.054
Credit_Bureau0.0030.030-0.037-0.0010.0090.067-0.037-0.048-0.0130.0020.0720.0030.0240.033-0.010NaN-0.077-0.030-0.0250.0130.003-0.0270.012-0.026-0.075-0.0210.0911.0000.020
Default0.000-0.022-0.0230.0000.000-0.0010.020-0.031-0.012-0.035-0.074-0.076-0.039-0.0540.0480.0010.022-0.0260.0110.0590.006-0.024-0.147-0.155-0.176-0.033-0.0540.0201.000
2025-11-28T10:23:07.747934image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
IDClient_IncomeCar_OwnedBike_OwnedActive_LoanHouse_OwnChild_CountCredit_AmountLoan_AnnuityPopulation_Region_RelativeAge_DaysEmployed_DaysRegistration_DaysID_DaysOwn_House_AgeMobile_TagHomephone_TagWorkphone_WorkingClient_Family_MembersCleint_City_RatingApplication_Process_DayApplication_Process_HourScore_Source_1Score_Source_2Score_Source_3Social_Circle_DefaultPhone_ChangeCredit_BureauDefault
ID1.0000.001-0.003-0.001-0.0010.000-0.006-0.001-0.0010.0010.0030.0020.0030.0020.003-0.002-0.0050.003-0.005-0.0020.001-0.001-0.001-0.004-0.0030.0020.0000.0010.000
Client_Income0.0011.0000.186-0.0030.0020.0070.0280.2920.3430.065-0.0570.043-0.049-0.024-0.1570.001-0.053-0.0130.029-0.187-0.0040.0730.0530.121-0.0620.0770.0320.047-0.016
Car_Owned-0.0030.1861.000-0.000-0.002-0.0080.1020.0900.1150.036-0.1050.004-0.060-0.013NaN-0.0040.011-0.0090.146-0.022-0.0040.013-0.0480.041-0.0080.0190.024-0.028-0.023
Bike_Owned-0.001-0.003-0.0001.0000.0020.0020.0030.004-0.0000.0000.000-0.000-0.0040.0000.006-0.004-0.002-0.0060.0040.000-0.0030.001-0.004-0.004-0.004-0.001-0.004-0.0010.000
Active_Loan-0.0010.002-0.0020.0021.000-0.0060.001-0.000-0.0010.0040.000-0.001-0.0000.001-0.0040.0030.0010.000-0.002-0.0030.0020.004-0.0010.0010.0020.0050.0050.0070.000
House_Own0.0000.007-0.0080.002-0.0061.000-0.007-0.031-0.0050.0190.0990.0240.014-0.002-0.008-0.002-0.112-0.0400.0070.0030.010-0.0860.0650.0020.0300.0120.0130.062-0.001
Child_Count-0.0060.0280.1020.0030.001-0.0071.000-0.0000.019-0.025-0.289-0.024-0.1380.0120.0040.0020.047-0.0340.7770.021-0.000-0.002-0.119-0.015-0.036-0.0100.010-0.0330.021
Credit_Amount-0.0010.2920.0900.004-0.000-0.031-0.0001.0000.6410.0370.0430.070-0.0040.004-0.0710.002-0.0280.0180.057-0.076-0.0030.0350.1070.0850.0160.0350.050-0.025-0.014
Loan_Annuity-0.0010.3430.115-0.000-0.001-0.0050.0190.6411.0000.037-0.0060.047-0.024-0.011-0.072-0.000-0.0260.0070.069-0.100-0.0000.0390.0750.0830.0080.0430.0510.0010.000
Population_Region_Relative0.0010.0650.0360.0000.0040.019-0.0250.0370.0371.0000.0260.0000.0240.010-0.0910.002-0.0130.053-0.016-0.353-0.0040.0920.0530.1290.0010.1070.0300.005-0.024
Age_Days0.003-0.057-0.1050.0000.0000.099-0.2890.043-0.0060.0261.0000.2090.2020.2210.0140.004-0.1360.032-0.216-0.0040.008-0.0650.4220.0690.1380.0030.0740.045-0.060
Employed_Days0.0020.0430.004-0.000-0.0010.024-0.0240.0700.0470.0000.2091.0000.0810.074-0.0310.0040.0150.0460.0120.006-0.002-0.0150.1570.0750.092-0.0000.1270.018-0.066
Registration_Days0.003-0.049-0.060-0.004-0.0000.014-0.138-0.004-0.0240.0240.2020.0811.0000.0650.026-0.000-0.0370.062-0.118-0.0540.0010.0090.1060.0430.072-0.0050.0510.014-0.030
ID_Days0.002-0.024-0.0130.0000.001-0.0020.0120.004-0.0110.0100.2210.0740.0651.0000.0060.003-0.0410.0290.0080.0090.002-0.0230.0900.0350.0930.0040.0770.023-0.045
Own_House_Age0.003-0.157NaN0.006-0.004-0.0080.004-0.071-0.072-0.0910.014-0.0310.0260.0061.000-0.005-0.013-0.0500.0050.1370.013-0.092-0.087-0.0890.016-0.0550.0300.0190.044
Mobile_Tag-0.0020.001-0.004-0.0040.003-0.0020.0020.002-0.0000.0020.0040.004-0.0000.003-0.0051.0000.0010.0020.0000.000-0.003-0.001NaNNaNNaN-0.002NaNNaN0.001
Homephone_Tag-0.005-0.0530.011-0.0020.001-0.1120.047-0.028-0.026-0.013-0.1360.015-0.037-0.041-0.0130.0011.0000.2900.0600.013-0.0140.026-0.063-0.017-0.050-0.0210.011-0.0640.022
Workphone_Working0.003-0.013-0.009-0.0060.000-0.040-0.0340.0180.0070.0530.0320.0460.0620.029-0.0500.0020.2901.000-0.012-0.076-0.0100.0460.0620.0550.0020.0460.048-0.023-0.026
Client_Family_Members-0.0050.0290.1460.004-0.0020.0070.7770.0570.069-0.016-0.2160.012-0.1180.0080.0050.0000.060-0.0121.0000.0280.001-0.011-0.075-0.000-0.019-0.0070.027-0.0160.008
Cleint_City_Rating-0.002-0.187-0.0220.000-0.0030.0030.021-0.076-0.100-0.353-0.0040.006-0.0540.0090.1370.0000.013-0.0760.0281.0000.009-0.215-0.089-0.255-0.009-0.121-0.0080.0150.057
Application_Process_Day0.001-0.004-0.004-0.0030.0020.010-0.000-0.003-0.000-0.0040.008-0.0020.0010.0020.013-0.003-0.014-0.0100.0010.0091.000-0.0180.002-0.0000.0000.002-0.0030.0050.005
Application_Process_Hour-0.0010.0730.0130.0010.004-0.086-0.0020.0350.0390.092-0.065-0.0150.009-0.023-0.092-0.0010.0260.046-0.011-0.215-0.0181.0000.0240.114-0.0270.056-0.007-0.026-0.019
Score_Source_1-0.0010.053-0.048-0.004-0.0010.065-0.1190.1070.0750.0530.4220.1570.1060.090-0.087NaN-0.0630.062-0.075-0.0890.0020.0241.0000.1520.1220.0390.0880.008-0.117
Score_Source_2-0.0040.1210.041-0.0040.0010.002-0.0150.0850.0830.1290.0690.0750.0430.035-0.089NaN-0.0170.055-0.000-0.255-0.0000.1140.1521.0000.0710.0650.110-0.018-0.117
Score_Source_3-0.003-0.062-0.008-0.0040.0020.030-0.0360.0160.0080.0010.1380.0920.0720.0930.016NaN-0.0500.002-0.019-0.0090.000-0.0270.1220.0711.0000.0030.070-0.045-0.133
Social_Circle_Default0.0020.0770.019-0.0010.0050.012-0.0100.0350.0430.1070.003-0.000-0.0050.004-0.055-0.002-0.0210.046-0.007-0.1210.0020.0560.0390.0650.0031.000-0.004-0.015-0.034
Phone_Change0.0000.0320.024-0.0040.0050.0130.0100.0500.0510.0300.0740.1270.0510.0770.030NaN0.0110.0480.027-0.008-0.003-0.0070.0880.1100.070-0.0041.0000.096-0.045
Credit_Bureau0.0010.047-0.028-0.0010.0070.062-0.033-0.0250.0010.0050.0450.0180.0140.0230.019NaN-0.064-0.023-0.0160.0150.005-0.0260.008-0.018-0.045-0.0150.0961.0000.015
Default0.000-0.016-0.0230.0000.000-0.0010.021-0.0140.000-0.024-0.060-0.066-0.030-0.0450.0440.0010.022-0.0260.0080.0570.005-0.019-0.117-0.117-0.133-0.034-0.0450.0151.000

Missing values

2025-11-28T10:22:16.883650image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
A simple visualization of nullity by column.
2025-11-28T10:22:19.387825image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-11-28T10:22:24.043038image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

IDClient_IncomeCar_OwnedBike_OwnedActive_LoanHouse_OwnChild_CountCredit_AmountLoan_AnnuityAccompany_ClientClient_Income_TypeClient_EducationClient_Marital_StatusClient_GenderLoan_Contract_TypeClient_Housing_TypePopulation_Region_RelativeAge_DaysEmployed_DaysRegistration_DaysID_DaysOwn_House_AgeMobile_TagHomephone_TagWorkphone_WorkingClient_OccupationClient_Family_MembersCleint_City_RatingApplication_Process_DayApplication_Process_HourClient_Permanent_Match_TagClient_Contact_Work_TagType_OrganizationScore_Source_1Score_Source_2Score_Source_3Social_Circle_DefaultPhone_ChangeCredit_BureauDefault
0121425096750.00.00.01.00.00.061190.553416.85AloneCommercialSecondaryMMaleCLHome0.02866313957.01062.06123.0383.0NaN110Sales2.02.06.017.0YesYesSelf-employed0.5680660.478787NaN0.018663.0NaN0
11213893620250.01.00.01.0NaN0.015282.001826.55AloneServiceGraduationMMaleCLHome0.00857514162.04129.07833.021.00.0101NaN2.02.03.010.0YesYesGovernment0.5633600.215068NaNNaNNaNNaN0
21218126418000.00.00.01.00.01.059527.352788.20AloneServiceGraduation dropoutWMaleCLFamily0.02280016790.05102.0NaN331.0NaN100Realty agents2.02.04.0NaNYesYesSelf-employedNaN0.5527950.3296550.0742277.00.00
31218892915750.00.00.01.01.00.053870.402295.45AloneRetiredSecondaryMMaleCLHome0.01055623195.0NaNNaN775.0NaN100NaN2.03.02.015.0YesYesNaNNaN0.1351820.631355NaN1700.03.00
41213338533750.01.00.01.00.02.0133988.403547.35AloneCommercialSecondaryMFemaleCLHome0.02071311366.02977.05516.04043.06.0100Laborers4.01.03.0NaNYesYesBusiness Entity Type 30.5081990.3011820.3556390.2021674.01.00
51219161411250.00.01.01.01.01.013752.00653.85AloneServiceSecondaryWFemaleCLHome0.01910113881.01184.03910.03910.0NaN100Laborers2.02.02.010.0YesYesOtherNaN0.6979280.4206110.0639739.00.00
61212808615750.01.01.00.01.00.0128835.003779.55AloneRetiredSecondarySMaleCLHome0.01661221323.0NaN113.04855.010.0100NaN1.02.03.014.0YesYesNaN0.7299130.6025450.5118920.2041NaN3.00
71221526413500.00.00.01.01.00.060415.203097.80AloneRetiredSecondaryMMaleCLHome0.00917522493.0NaN12617.05280.0NaN101NaN2.02.04.015.0YesYesNaN0.7114680.6575080.549597NaN1687.04.00
81215914713500.01.01.00.01.01.045000.001200.15RelativeCommercialGraduationMFemaleCLHome0.006008NaN7889.05455.02665.014.0101Sales3.02.04.013.0YesYesSelf-employed0.4757270.6375940.5531650.16701611.00.00
91213054712150.00.00.00.01.00.016320.151294.65AloneRetiredSecondaryWMaleCLHome0.01661220507.0NaN2834.04053.0NaN100NaN1.02.0NaN9.0YesYesNaN0.6822850.0633430.080650NaN533.05.00
IDClient_IncomeCar_OwnedBike_OwnedActive_LoanHouse_OwnChild_CountCredit_AmountLoan_AnnuityAccompany_ClientClient_Income_TypeClient_EducationClient_Marital_StatusClient_GenderLoan_Contract_TypeClient_Housing_TypePopulation_Region_RelativeAge_DaysEmployed_DaysRegistration_DaysID_DaysOwn_House_AgeMobile_TagHomephone_TagWorkphone_WorkingClient_OccupationClient_Family_MembersCleint_City_RatingApplication_Process_DayApplication_Process_HourClient_Permanent_Match_TagClient_Contact_Work_TagType_OrganizationScore_Source_1Score_Source_2Score_Source_3Social_Circle_DefaultPhone_ChangeCredit_BureauDefault
1218461220438912150.00.01.00.01.00.025470.001462.05AloneRetiredGraduation dropoutSMaleCLHome0.02516424123.0NaN9523.0795.0NaN100NaN1.02.0NaN9.0YesYesNaN0.720885NaNNaN0.0711NaN0.00
1218471218694115750.01.00.01.01.00.026128.801283.85AloneCommercialNaNMMaleCLHomeNaN14025.01107.0507.04514.024.0100ManagersNaN1.05.09.0YesYesBusiness Entity Type 3NaN0.7298250.441836NaN1175.01.00
1218481211072318000.01.01.00.00.01.027302.402169.90AloneServiceSecondaryMFemaleCLHome0.03579211073.01521.04883.03602.023.0100Sales3.02.02.014.0YesYesHousingNaN0.6252070.306202NaN1718.02.00
1218491218346410350.00.01.00.00.00.018792.901736.55AloneServiceGraduation dropoutSMaleCLMunicipal0.0100329204.0763.03773.01874.0NaN101Sales1.02.03.011.0YesYesSelf-employed0.1627600.621042NaN0.3340774.0NaN0
1218501213640612150.00.00.01.00.00.078192.002383.65AloneRetiredSecondarySMaleCLHome0.01885023943.0NaN1213.04011.0NaN100NaN1.02.02.011.0YesYesNaNNaN0.6782490.2837120.05151581.02.00
1218511220771429250.00.00.0NaN1.00.0107820.003165.30RelativeServiceSecondaryMFemaleCLHome0.03132912889.02863.02661.02943.0NaN100Laborers2.02.04.016.0YesNoBusiness Entity Type 2NaN0.1735270.1841160.0577NaN1.01
1218521217376515750.00.01.01.00.00.0104256.003388.05AloneCommercialGraduationMFemaleCLHome0.0182098648.0636.0902.01209.0NaN110Sales2.03.04.012.0YesYesSelf-employedNaN0.3715590.4066170.08254.00.00
121853121039378100.00.01.00.01.01.055107.902989.35AloneGovt JobSecondaryMMaleCLHome0.0080689152.01623.03980.0353.0NaN100High skill tech3.03.05.011.0NoNoTrade: type 60.1690490.048079NaNNaNNaNNaN0
1218541217062338250.01.01.00.01.00.045000.002719.35AloneServiceGraduationMFemaleCLHome0.02866310290.0847.0895.02902.04.0100Sales2.02.01.012.0YesYesBusiness Entity Type 30.1827370.1035380.0774990.0979NaN2.00
121855121056109000.01.01.01.01.01.062428.954201.65AloneCommercialSecondarySMaleCLHome0.01802914772.0498.08679.05025.06.0100Managers2.03.04.06.0YesYesBusiness Entity Type 3NaN0.5564140.2985950.1031805.00.00